The socio-technical opportunities and threats of crowdsensing

Abstract

The exponential growth of mobile crowd sensing (MCS) has provided unparalleled opportunities to collect large-scale data through a network of mobile devices, empowering diverse applications in smart cities, healthcare, and environmental monitoring. However, the inherently participatory nature of MCS raises critical privacy concerns, as sensitive user information is often at risk of exposure. This literature review examines recent advancements in employing machine learning techniques to enhance privacy preservation in MCS frameworks. It explores methods such as federated learning, differential privacy, and encryption-enhanced neural networks that aim to minimize data leakage while maintaining model accuracy. Additionally, this review analyzes the efficacy and limitations of various privacy-preserving algorithms, particularly regarding their adaptability to different MCS contexts and their impact on computational overhead and communication efficiency. Through a comprehensive synthesis of current studies, this review highlights emerging trends, identifies research gaps, and suggests future directions for developing robust privacy-preserving machine learning models tailored to the unique demands of MCS systems.